We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
安徽省PM<sub>2.5</sub>浓度反演方法对比及时空变化研究.
- Authors
赵月娇; 赵萍; 徐凯健; 周鹏; 申鹏举; 于婉婉; 陈国旭
- Abstract
In order to explore the spatial and temporal distribution characteristics of PM2.5 in Anhui Province, based on ground-based PM2.5, AOD, vegetation cover products and meteorological elements data from 2015 to 2020, the accuracy of three models, namely, multi-scale geographically weighted regression model (MGWR), random forest and fully connected neural network, was compared. The full-link neural network model was used to invert PM2.5 concentration, analyze the spatiotemporal variation characteristics of PM2.5 concentration, and the influence of each factor on PM2.5 concentration. The results show that the fully connected neural network model has the highest accuracy among the three models. From 2015 to 2020, PM2.5 concentration decreased from an average of 51.29 μg/m3 to 36.71 μg/m3. On a seasonal scale, PM2.5 concentration was higher in winter than in spring and autumn than in summer. The spatial pattern was northern Anhui > central Anhui > southern Anhui. Any interaction of two of the 10 influencing factors has a greater impact on PM2.5 concentration than a single factor. AOD has the greatest impact on PM2.5, and each factor has different degrees of influence on PM2.5 in different seasons.
- Subjects
ANHUI Sheng (China); ARTIFICIAL neural networks; SPRING; AUTUMN; RANDOM forest algorithms; GROUND vegetation cover; SUMMER
- Publication
Environmental Science & Technology (10036504), 2022, Vol 45, Issue 6, p171
- ISSN
1003-6504
- Publication type
Article
- DOI
10.19672/j.cnki.1003-6504.0013.22.338